High spatial resolution prediction

ABSTRACT

A method, system, and computer program product for managing resources by obtaining a high spatial resolution estimate of behavior adoption are described. The method includes obtaining a low-resolution estimate with a fixed geographic scale, selecting a sample of customers based on the low-resolution estimate, implementing a statistical model to obtain relative probability of adoption of the behavior by each of the sample of customers, and generating a weighted random realization from the sample of customers, the weighted random realization being weighted based on the relative probability of adoption. The method includes iteratively implementing the selecting the sample of customers, the implementing the statistical model, and the generating the weighted random realization to obtain a set of the weighted random realizations, and obtaining the high spatial resolution estimate, providing greater resolution than the low-resolution estimate at a location of interest, based on the set of the weighted random realizations.

DOMESTIC PRIORITY

This application is a continuation of U.S. application Ser. No.14/855,812 filed Sep. 16, 2015 which claims the benefit of U.S.Provisional Application Ser. No. 62/153,775 filed Apr. 28, 2015, thedisclosures of both of which are incorporated by reference herein intheir entirety.

BACKGROUND

The present invention relates to prediction, and more specifically, tohigh spatial resolution prediction.

Estimates or projections are often available for a number of behaviors.For example, adoption of a new technology (e.g., cellular technology,green energy technology) or conduct (e.g., recycling) may be projectedover a period of time. These projections may help ensure theavailability and procurement of necessary components, for example.

SUMMARY

Embodiments include a computer implemented method, system, and computerprogram product to manage resources by obtaining a high spatialresolution estimate of behavior adoption. The method includes obtaininga low-resolution estimate with a fixed geographic scale, thelow-resolution estimate indicating a number of adoptees of the behaviorin a specified time period, selecting a sample of customers based on thelow-resolution estimate, implementing, using a processor, a statisticalmodel to obtain relative probability of adoption of the behavior by eachof the sample of customers, and generating a weighted random realizationfrom the sample of customers, the weighted random realization beingweighted based on the relative probability of adoption. The method alsoincludes iteratively implementing the selecting the sample of customers,the implementing the statistical model, and the generating the weightedrandom realization to obtain a set of the weighted random realizations,and obtaining the high spatial resolution estimate, providing greaterresolution than the low-resolution estimate at a location of interest,based on the set of the weighted random realizations.

Additional features and advantages are realized through the techniquesof the present invention. Other embodiments and aspects of the inventionare described in detail herein and are considered a part of the claimedinvention. For a better understanding of the invention with theadvantages and the features, refer to the description and to thedrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter which is regarded as the invention is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The forgoing and other features, and advantages ofthe invention are apparent from the following detailed description takenin conjunction with the accompanying drawings in which:

FIG. 1 is a process flow of a method of managing resources based on ahigh spatial resolution prediction of behavior according to embodiments;

FIG. 2 shows high-level processes associated with executing thestatistical model according to embodiments;

FIG. 3 details processing of inputs performed to implement thestatistical model according to embodiments; and

FIG. 4 shows an exemplary system to implement resource management basedon high spatial resolution prediction according to embodiments.

DETAILED DESCRIPTION

As noted above, projections and estimates of behavior may providegeneral information about demand for a certain technology or serviceover a time period. However, this general information is less helpful inplanning or preparation for a projected trend at a regional level. Forexample, a projection that 100,000 to 200,000 residents will adopt agreen energy option by 2020 does not clarify where infrastructurechanges related to the adoption should be focused. The estimate alsodoes not help target advertising based on likelihood of adoption, forexample. While the initial projection or estimate may not provideassociated spatial information, other information may be available. Forexample, with reference to the projection of adoption to a green energytechnology, information may be available on people who have alreadyadopted the technology. Embodiments of the systems and methods hereinrelate to combining a (geographically) fixed scale estimate withstatistical modeling to obtain predictions at high spatial resolution.These predictions of behavior help to manage resources more effectivelyto meet future demand and address shifts in technology usage. Based onembodiments detailed herein, given a projection of 100,000 to 200,000residents adopting a green energy technology by 2020, a prediction maybe made that 30,000 to 35,000 people will adopt the technology in NewYork City, for example. While the green energy adoption is used forexplanatory purposes, the embodiments detailed herein apply, as well, toany number of applications. The outputs facilitated by the detailedembodiments include a prediction of adoption rate (or whatever theinitial projection relates to) with high spatial resolution (e.g., arange of numbers of people, households, customers per region, city, zipcode) and an indication of uncertainty that is communicated through therange or granularity in the prediction.

FIG. 1 is a process flow of a method of managing resources based on ahigh spatial resolution prediction of behavior according to embodimentsdetailed herein. At block 110, the processes include obtaining alow-resolution estimate of a behavior of interest. For example, thebehavior of interest may be adoption of wind energy and thelow-resolution estimate—that between 100,000 and 200,000 residents inthe United States will use by technology in 2020—may be obtained throughpolls or other sampling that provides an estimate with a fixedgeographic scale (e.g., the United States, in the example). Selecting asample, at block 120, includes sampling customers according to thelow-resolution estimate (obtained at block 110). For example, if 200,000people in the United States are expected to adopt a behavior (e.g.,switch to wind generation) by 2020 according to the low-resolutionestimate, then a random sample of 200,000 people may represent arealization. In alternate embodiments, the number sampled as the randomrealization need not be the same number as in the low-resolutionestimate.

Generating a weighted random realization, at block 125, includesexecuting a statistical model, at block 130, based on the selectedsample (at block 120). As the arrow between blocks 120 and 130indicates, the statistical model is implemented (at block 130) based oninputs relating to the sample of customers (e.g., people, households)generated (at block 120). The statistical model is further discussedbelow with reference to FIG. 2. The weighting to generate the weightedrandom realization, at block 125, ensures that customers who are morelikely to adopt the behavior of interest are more likely to be part ofthe random realization. Until an endpoint is reached, at block 140,selecting a sample and generating the weighted random realizations(blocks 120 through 130) are repeated iteratively. For example, a set of10,000 realizations may ultimately be obtained through the iterativeprocess. While each random realization will have a different sampling ofcustomers, customers given higher weight according to the statisticalmodel (customers who are more likely to adopt the behavior of interest)may be part of more random realizations than other customers. Theiterative process may end (at 140) based on a predefined number ofrandom realizations being obtained, for example. In alternateembodiments, other parameters may be used to determine when to stopobtaining random realizations. Alternatively, the iterative process mayend when the additional iteration adds negligible changes over theprevious iteration. Or the distribution of the random realizations haveconverged to the desired probability distribution.

Once a set of random realizations has been obtained through theiterative process, summarizing the realizations, at block 150, refers toexamining the samples within a specified high-resolution spatialframework (e.g., state, city, county, zip code). Table 1 indicates asummary of a set of 10 realizations within the specified spatialframework of New York City:

TABLE 1 Exemplary summary of realizations number of adopters of therealization number behavior in New York City 1 10,000 2 9,788 3 8,567 49,000 5 8,000 6 8,544 7 8,800 8 9,200 9 9,500 10 9,400Based on the summary (at block 150), providing a high-resolutionestimate and uncertainty, at block 160, may be completed. Thehigh-resolution estimate represents the range captured by a highpercentage of realizations (e.g., 90% of the realizations). Thepercentage may be specified or determined based on other factors. Anexemplary factor that affects the percentage of the realizations thatare considered is he the accuracy requirements for the investment andupgrade decisions on the existing infrastructure. For example, anelectric distribution company may need 95% confidence to installoverhead lines to support the anticipated demand increase from thehigh-resolution estimate for the purpose of the business justification.Thus, 95% of the realizations obtained (at block 150) may be used todetermine the high-resolution estimate. In the example shown in Table 1,the high-resolution estimate provides spatial resolution at the level ofa city (namely, New York City). The exemplary high-resolution estimateof adopters of the behavior in New York City, according to 90% of therealization summaries shown in Table 1, may be 9199 (average of thehighest 90% of realizations) or 8544 (the lowest realization among thehighest 90% of realizations). That is, even after a percentage of therealizations is selected, the way that the percentage of realizations isused to obtain a high spatial resolution estimate may be chosen based onbusiness needs. For example, the highest 90% may be used as anaggressive indicator of investment needs while the lowest 90% may beused as a conservative indicator of investment needs. Uncertainty isindicated by the range of realization summary values. In the exampleshown in Table 1, the uncertainty is 8,000 to 10,000. The wider therange, the more uncertainty there is in the high-resolution estimate.Managing resources, at block 170, is based on the high-resolutionestimate and the uncertainty. More planning and action may be takenbased on an estimate with less uncertainty than one with moreuncertainty. Depending on the behavior that is estimated to be adopted,the planning may include movement of resources to the regions where themost adoption of the behavior is estimated, planning to buildinfrastructure, and the like.

FIG. 2 shows high-level processes associated with executing thestatistical model (FIG. 1, 130) according to embodiments. Thestatistical model (FIG. 1, 130) is applied to information about thesample of customers (FIG. 1, 120). At block 210, receiving inputs(associated with the sample of customers) may include receivinginformation about demographics (e.g., age, number of family members,income, education), properties (e.g., building square footage and cost,number or rooms, current energy usage), and behavior attributes (e.g.,proxy measures of interest in the environment, frugality), for example.The inputs may be based on self-reporting (e.g., survey resultsregarding income), publicly available information (e.g., public recordsregarding the specifications of a building such as a home), and frommemberships (e.g., in environmental groups), for example. Many of thesame sources currently used for marketing research may be used to obtaininputs. Applying a model to these inputs, at block 220, includesimplementing a support vector machine, logistic regression, neuralnetworks, or random forests, for example. The embodiments are notlimited in the statistical models that may be applied. Application of amodel to the inputs results in an indication, at block 230, of therelative probability of adoption of the behavior of interest for a givencustomer. For example, the behavior of interest may be the adoption ofphotovoltaic technology (solar panels). In this case, the inputs to thestatistical model, like the exemplary inputs discussed above, mayindicate both the need and interest of customers within a samplerealization to adopt the technology. As noted with reference to block125, the output of the statistical model and, specifically, the relativeprobability provided for customers within a sample realization,facilitates weighting the realization (FIG. 1, 120).

FIG. 3 details processing of inputs performed to implement thestatistical model according to embodiments. The exemplary statisticalmodel used for explanatory purposes is related to adoption ofphotovoltaic (PV) or solar energy. Exemplary inputs (FIG. 2, 210) areshown and include electrical usage and a number of attributes. Thenumbers of each type of attribute are indicated. For example, over 20demographic attributes (e.g., age, number of family members, income,education) may be obtained. Over 700 lifestyle attributes may beobtained, as indicated in FIG. 3. Exemplary lifestyle attributes mayinclude the number of hours spent in the building in a day, donationpatterns to sustainability activities, purchasing behavior ofgreen/sustainable products and services. Hidden variables 310 areobtained from the inputs. Hidden variables 310 refer to the informationimplied by the inputs or, put another way, information that may bederived from the inputs. In the exemplary case of the behavior ofinterest being adoption of PV (solar energy), the hidden variables 310may include acceptability to PV systems, changes in consumptionpatterns, and viral exposure from neighbors and economic conditions.Viral exposure refers to the influence that a sample consumer'sneighbors or economic conditions may have on behavior adoption. Forexample, people tend to purchase items that neighbors own. Thus, a PVsystem may be purchased partly due to the fact that neighbors own PVsystems (the neighbors' purchases may increase the likelihood ofpurchase by the sample customer). Deep learning nodes 320 aremulti-layer nodes that represent a proxy of hidden variables. Accordingto one exemplary embodiment, the first layer is created by combining thevariables from the raw data stream, the second layer is created bycombining the composite variables from the first layer and so on.Modeling components 330 are obtained from known solar panel installationcases in the exemplary statistical model. The modeling components mayinclude predictive models using historical PV system adoptions in thecontext of the building attributes, lifestyle attributes, and energyconsumption patterns. The model is applied (FIG. 2, 220) and providedwith the information obtained from the inputs (FIG. 2, 210) to providethe relative probability of adoption of the behavior of interest (FIG.2, 230).

FIG. 4 shows an exemplary system 400 to implement resource managementbased on high spatial resolution prediction according to embodiments.The exemplary system 400 includes one or more memory devices 410 thatstore instructions and data, and one or more processors 420 thatimplement the stored instructions and other inputs. The exemplary system400 may also include input interfaces 440 (e.g., keyboard) and outputinterfaces 430 (e.g., display device). The interfaces may facilitatewireless communication with other systems and databases, for example,and may be used to obtain inputs for the statistical model.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of onemore other features, integers, steps, operations, element components,and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

The flow diagrams depicted herein are just one example. There may bemany variations to this diagram or the steps (or operations) describedtherein without departing from the spirit of the invention. Forinstance, the steps may be performed in a differing order or steps maybe added, deleted or modified. All of these variations are considered apart of the claimed invention.

While the preferred embodiment to the invention had been described, itwill be understood that those skilled in the art, both now and in thefuture, may make various improvements and enhancements which fall withinthe scope of the claims which follow. These claims should be construedto maintain the proper protection for the invention first described.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

What is claimed is:
 1. A computer implemented method of managingresources by updating a legacy electrical network based on a highspatial resolution estimate of renewable asset adoption, the methodcomprising: obtaining a low-resolution estimate with a fixed geographicarea, the low-resolution estimate indicating a number of adoptees ofrenewable assets in a specified time period, each renewable asset of therenewable assets capable of providing energy to the legacy electricalnetwork; selecting a sample of customers within the fixed geographicarea, the sample of customers satisfying the low-resolution estimate;implementing, using a processor, an artificial neural network model toobtain a relative probability of adoption of renewable assets by each ofthe sample of customers, the implementing the artificial neural networkmodel including: obtaining inputs associated with the sample ofcustomers; deriving hidden variables implied by the inputs associatedwith the sample of customers; providing the hidden variables to aplurality of deep learning nodes; generating, using the processor, aweighted random realization from the of customers, the weighted randomrealization being weighted based on the relative probability ofadoption; iteratively implementing the selecting the sample ofcustomers, the implementing the artificial neural network model, and thegenerating the weighted random realization to obtain a set of theweighted random realizations; obtaining a first high spatial resolutionestimate from the set of weighted random realizations based on athreshold range, the first high spatial resolution estimate beinggenerated from a first subset of customers in the sample of customers,the first subset of customers located in a particular geographiclocation within the fixed geographic area, the first high spatialresolution estimate representing an increasing trend in the probabilityof adoption; predicting a distribution of renewable asset adoption inthe particular geographic location; predicting distributions of thelegacy electrical network in the particular geographic location based onthe first high spatial resolution estimate; predicting disruptions andoutages caused by renewable assets based on the predicted distributionof renewable asset adoption in the particular geographic location; andpresenting a roll-out plan for installing any number of overhead linesto be coupled to the legacy electrical network in the particulargeographic location based on the first high spatial resolution estimateto and the predicted disruptions and outages caused by the renewableassets to reduce a risk of load caused by the renewable assets.
 2. Thecomputer implemented method according to claim 1, wherein the selectingthe sample of customers includes selecting a sample size equal to thenumber of adoptees of a behavior in the specified time period.
 3. Thecomputer implemented method according to claim 1, wherein the obtainingthe inputs includes obtaining one or more of a demographic informationand a behavioral attributes.
 4. The computer implemented methodaccording to claim 1, wherein the obtaining the inputs includesdetermining changes in consumption patterns for the sample of customers.5. The computer implemented method according to claim 1, wherein theobtaining the high spatial resolution estimate includes obtaining acount of adopting customers among the sample of customers for eachiteration at a given location.
 6. The computer implemented methodaccording to claim 5, wherein the obtaining the first high spatialresolution estimate includes obtaining a range of the count of adoptingcustomers over all the iterations as an uncertainty in the high spatialresolution estimate.
 7. The computer implemented method according toclaim 1 further comprising: obtaining a second high spatial resolutionestimate from the set of weighted random realization based on thethreshold range, the second high spatial resolution estimate generatedfrom a second subset of customers in the sample of customers, the secondsubset of customers located in the particular geographic location, thesecond high spatial resolution estimate representing the increasingtrend in probability adoption.
 8. The computer implemented methodaccording to claim 7, wherein the managing resources by planning theupdate of the legacy electrical network in the particular geographiclocation is further based on a range between the first high spatialresolution estimate and the second high spatial resolution estimate.